OCR (Optical character recognition) is a process for obtaining textual information through analysis and recognition of an image file of textual information. This technology was initially used by a postal system to recognize zip codes to implement an automatic mail sorting function. Along with the rapid development of optical imaging devices (e.g., a scanner, a digital camera, a mobile phone, etc.), OCR has been used more popularly. For example, the 6th national census in 2010 used the OCR technology to perform an automatic information input operation for about 800 millions of forms, which greatly reduced manpower costs.
The OCR technology usually includes the following operations: 1) pre-processing an input image, which includes gray processing; 2) extracting a textual field image via a layout analysis; 3) generating a binary textual field image; 4) perform character segmentation for the binary textual field image; and 5) outputting a result from character recognition. The character segmentation is one of many critical stages in the entire character recognition process, having a very great influence in the performance of the final recognition. The character segmentation is to segment each character image (which includes a Chinese character, an English letter, a number, a punctuation, etc.) from a single textual image. The quality of the character segmentation directly affects the performance of an OCR system. Apparently, a recognizer can obtain an accurate recognition only when an associated segmentation is accurate. An incorrectly segmented character cannot be recognized correctly. Currently, technologies of recognizing a single character have been very mature. According to development results of practical systems, errors of final recognition results of the systems are mostly due to errors in segmentation. Therefore, a good character segmentation method is particularly important.
Currently, common character segmentation methods mainly include the following types: